Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed patients still fail to respond even after months to years of searching to find an effective combination. To aid in the identification of potentially effective antidepressant combinations, we created a computational model of the monoaminergic neurotransmitter (serotonin, norepinephrine, and dopamine), stress-hormone (cortisol), and male sex hormone (testosterone) systems. The model was trained via machine learning to represent a broad range of empirical observations. Neuroadaptation to chronic drug administration was simulated through incremental adjustments in model parameters that corresponded to key regulatory components of the neurotransmitter and neurohormone systems. Analysis revealed that neuroadaptation in the model depended on all of the regulatory components in complicated ways, and did not reveal any one or a few specific components that could be targeted in the design of antidepressant treatments. We used large sets of neuroadapted states of the model to screen 74 different drug and hormone combinations and identified several combinations that could potentially be therapeutic for a higher proportion of male patients than SSRIs by themselves.

Computational Analysis of Therapeutic Neuroadaptation to Chronic Antidepressant in a Model of the Monoaminergic Neurotransmitter and Stress Hormone Systems

The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavily on trial-and-error. Unfortunately, the drug combinations prescribed today fail to provide relief for many depressed patients. In order to identify potentially more effective treatments, we developed a computational model of the monoaminergic neurotransmitter and stress-steroid systems that neuroadapts to chronic administration of combinations of antidepressant drugs and hormones by adjusting the strengths of its transmitter-system components (TSCs). We used the model to screen 60 chronically administered drug/hormone pairs and triples, and identified as potentially therapeutic those combinations that raised the monoamines (serotonin, norepinephrine, and dopamine) but lowered cortisol following neuroadaptation in the model. We also evaluated the contributions of individual and pairs of TSCs to therapeutic neuroadaptation with chronic SSRI using sensitivity, correlation, and linear temporal-logic analyses. All three approaches revealed that therapeutic neuroadaptation to chronic SSRI is an overdetermined process that depends on multiple TSCs, providing a potential explanation for the clinical finding that no single antidepressant regimen alleviates depressive symptoms in all patients.